TOWARDS REAL-TIME ASSESSMENT: WEARABLE-BASED ESTIMATION OF 3D KNEE KINETICS IN RUNNING AND THE INFLUENCE OF PREPROCESSING WORKFLOWS

Authors

Lucas Höschler, University of SalzburgFollow
Christina Halmich, Salzburg Research Forschungsgesellschaft mbH, University of SalzburgFollow
Christoph Schranz, Salzburg Research Forschungsgesellschaft mbHFollow
Julian Fritz, Adidas AGFollow
Anne Koelewijn, Friedrich-Alexander University Erlangen-NürnbergFollow
Hermann Schwameder, University of SalzburgFollow

Category

Wearable Technology

Document Type

Paper

Abstract

This study aimed to estimate knee kinetics in recreational runners during treadmill running based on seven IMUs and pressure insoles using convolutional neural networks (CNN) with two input segmentations. Ground-truth knee moments of 19 runners during sloped and level treadmill running were calculated by conventional lab-based methods. We trained two CNNs on (1) step-segmented and (2) continuously windowed inputs and investigated differences in their joint moment estimations to ground-truth calculations. For both input segmentations, the predictions errors (nRMSE) were below 0.10 and 0.25 for the sagittal and non-sagittal planes, respectively. The continuous inputs led to a slightly decreased accuracy during stance phases (nRMSE

Recommended Citation

Höschler, Lucas; Halmich, Christina; Schranz, Christoph; Fritz, Julian; Koelewijn, Anne; and Schwameder, Hermann (2024) "TOWARDS REAL-TIME ASSESSMENT: WEARABLE-BASED ESTIMATION OF 3D KNEE KINETICS IN RUNNING AND THE INFLUENCE OF PREPROCESSING WORKFLOWS," ISBS Proceedings Archive: Vol. 42: Iss. 1, Article 72.
Available at: https://commons.nmu.edu/isbs/vol42/iss1/72